Exploiting key points supervision and grouped feature fusion for multiview pedestrian detection

Highlights：

• We introduce a method to accomplish multiview pedestrian detection using key points regression and the correlation of overlapping fields of multiple views, which achieves effective multiview features aggregation under the action of three types of key points and grouped feature fusion modules.

• The proposed key points supervision method regresses pedestrians into three points, which can effectively handle pedestrian detection under occlusion.

• The proposed grouping feature fusion module is the first attempt to apply the correlation of multiview overlapping fields to multiview feature aggregation, the feature enhancement and fusion for this region can help reduce object ambiguity.

• Compared to state-of-the-art methods, our method achieves superior performance with only a small increase in computation.